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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Количественная оценка перекрестных сетевых эффектов для нетрансакционных платформ

Вестник Московского университета. Серия 6: Экономика. 2022. № 4. С. 17–38.
Рожкина В. С., Golovanova S., Korneeva D.

The analysis of cross-network effects is important for considering the impossibility of their direct observation and the influence of cross-network effects on the values of all tests in competition policy, pricing practice and merger valuation. The article summarizes the experience of quantifying cross-network effects for non-transactional platforms. This paper systematizes methods for assessing cross-network effects (regression equation systems, features of data collection, control variables) used in the study of the mutual influence of the media audience (newspapers, magazines, television channels) and advertising. The results of the analysis reflect the technical feasibility of this goal and the requirements for the initial data and the competence of the expert in the application of quantitative methods. The article also summarizes the results of empirical estimates, which in most cases coincides with the predictions of the theory about the sign (direction) of cross-network effects.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: quantitative methodsоценка спросаdemand estimationперекрестные сетевые эффектыколичественные методымногосторонние платформыmulti-sided marketsindirect network effects
Publication based on the results of:
The evidence-based competition policy in the markets controlled by digital platforms (The international and Russian practice) (2022)
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